Overview

Dataset statistics

Number of variables14
Number of observations52560
Missing cells932
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.6 MiB
Average record size in memory112.0 B

Variable types

DateTime1
Numeric13

Alerts

Power (kW) is highly overall correlated with Rear bearing temperature (°C) and 6 other fieldsHigh correlation
Wind direction (°) is highly overall correlated with Nacelle position (°)High correlation
Nacelle position (°) is highly overall correlated with Wind direction (°)High correlation
Rear bearing temperature (°C) is highly overall correlated with Power (kW) and 4 other fieldsHigh correlation
Rotor speed (RPM) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Generator RPM (RPM) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Front bearing temperature (°C) is highly overall correlated with Power (kW) and 5 other fieldsHigh correlation
Tower Acceleration X (mm/ss) is highly overall correlated with Power (kW) and 4 other fieldsHigh correlation
Wind speed (m/s) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Tower Acceleration y (mm/ss) is highly overall correlated with Power (kW) and 5 other fieldsHigh correlation
# Date and time has unique valuesUnique
blade_angle has 26317 (50.1%) zerosZeros
Rotor speed (RPM) has 1111 (2.1%) zerosZeros

Reproduction

Analysis started2023-07-08 11:55:39.910902
Analysis finished2023-07-08 11:55:57.804920
Duration17.89 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

# Date and time
Date

Distinct52560
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size410.8 KiB
Minimum2019-01-01 00:00:00
Maximum2019-12-31 23:50:00
2023-07-08T17:25:57.853534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:57.951390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Power (kW)
Real number (ℝ)

Distinct52385
Distinct (%)99.8%
Missing71
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean674.12364
Minimum-15.816108
Maximum2076.3194
Zeros0
Zeros (%)0.0%
Negative4456
Negative (%)8.5%
Memory size410.8 KiB
2023-07-08T17:25:58.058379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-15.816108
5-th percentile-1.1053409
Q1158.32399
median471.31021
Q31051.5157
95-th percentile2001.3458
Maximum2076.3194
Range2092.1355
Interquartile range (IQR)893.19168

Descriptive statistics

Standard deviation625.12329
Coefficient of variation (CV)0.92731252
Kurtosis-0.43050787
Mean674.12364
Median Absolute Deviation (MAD)378.65029
Skewness0.86572497
Sum35384076
Variance390779.13
MonotonicityNot monotonic
2023-07-08T17:25:58.155248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.9314910233 3
 
< 0.1%
-1.024853528 3
 
< 0.1%
-1.042459026 3
 
< 0.1%
-2.130799055 3
 
< 0.1%
-1.540214527 2
 
< 0.1%
-1.055263022 2
 
< 0.1%
-0.925089021 2
 
< 0.1%
-0.5158945158 2
 
< 0.1%
2034.995068 2
 
< 0.1%
-0.6012545124 2
 
< 0.1%
Other values (52375) 52465
99.8%
(Missing) 71
 
0.1%
ValueCountFrequency (%)
-15.81610751 1
< 0.1%
-15.30395741 1
< 0.1%
-15.05295859 1
< 0.1%
-13.85164614 1
< 0.1%
-13.71490617 1
< 0.1%
-13.6329247 1
< 0.1%
-13.61896758 1
< 0.1%
-13.60926056 1
< 0.1%
-13.60628651 1
< 0.1%
-13.53855395 1
< 0.1%
ValueCountFrequency (%)
2076.319403 1
< 0.1%
2075.483887 1
< 0.1%
2074.425403 1
< 0.1%
2073.468756 1
< 0.1%
2073.172607 1
< 0.1%
2073.075525 1
< 0.1%
2072.071417 1
< 0.1%
2071.897467 1
< 0.1%
2071.52774 1
< 0.1%
2071.329822 1
< 0.1%

Wind direction (°)
Real number (ℝ)

Distinct52487
Distinct (%)> 99.9%
Missing72
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean203.61887
Minimum0.0072405917
Maximum359.98983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:25:58.256608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0072405917
5-th percentile32.067181
Q1150.8461
median219.33637
Q3267.38539
95-th percentile330.14481
Maximum359.98983
Range359.98259
Interquartile range (IQR)116.53929

Descriptive statistics

Standard deviation89.145032
Coefficient of variation (CV)0.43780339
Kurtosis-0.53275356
Mean203.61887
Median Absolute Deviation (MAD)56.591531
Skewness-0.53690865
Sum10687547
Variance7946.8366
MonotonicityNot monotonic
2023-07-08T17:25:58.356613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
277.2627869 2
 
< 0.1%
277.1826694 1
 
< 0.1%
270.5378509 1
 
< 0.1%
266.473391 1
 
< 0.1%
265.8480923 1
 
< 0.1%
268.1874589 1
 
< 0.1%
273.3554712 1
 
< 0.1%
270.9961185 1
 
< 0.1%
277.172732 1
 
< 0.1%
275.4750913 1
 
< 0.1%
Other values (52477) 52477
99.8%
(Missing) 72
 
0.1%
ValueCountFrequency (%)
0.007240591718 1
< 0.1%
0.02455494917 1
< 0.1%
0.03210036848 1
< 0.1%
0.04590088129 1
< 0.1%
0.05243898567 1
< 0.1%
0.06967542867 1
< 0.1%
0.08268782613 1
< 0.1%
0.1143776964 1
< 0.1%
0.1395000834 1
< 0.1%
0.1471973775 1
< 0.1%
ValueCountFrequency (%)
359.9898306 1
< 0.1%
359.9894311 1
< 0.1%
359.9610264 1
< 0.1%
359.9074395 1
< 0.1%
359.9064448 1
< 0.1%
359.8915148 1
< 0.1%
359.8671657 1
< 0.1%
359.866624 1
< 0.1%
359.8444626 1
< 0.1%
359.84084 1
< 0.1%

Nacelle position (°)
Real number (ℝ)

Distinct14455
Distinct (%)27.5%
Missing72
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean204.78981
Minimum0.062640295
Maximum359.98847
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:25:58.462345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.062640295
5-th percentile33.168494
Q1154.99733
median219.75381
Q3269.33461
95-th percentile332.75181
Maximum359.98847
Range359.92583
Interquartile range (IQR)114.33728

Descriptive statistics

Standard deviation89.47915
Coefficient of variation (CV)0.43693166
Kurtosis-0.52292708
Mean204.78981
Median Absolute Deviation (MAD)56.561999
Skewness-0.53844997
Sum10749007
Variance8006.5184
MonotonicityNot monotonic
2023-07-08T17:25:58.563353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
206.5835266 144
 
0.3%
47.4370575 122
 
0.2%
163.7785645 101
 
0.2%
43.04681396 99
 
0.2%
207.6810913 95
 
0.2%
112.1926041 94
 
0.2%
237.3153381 92
 
0.2%
217.5586548 90
 
0.2%
84.75294495 85
 
0.2%
298.7790222 85
 
0.2%
Other values (14445) 51481
97.9%
ValueCountFrequency (%)
0.06264029456 1
 
< 0.1%
0.1288120499 1
 
< 0.1%
0.2414538711 6
 
< 0.1%
0.2414805889 19
 
< 0.1%
0.2414855957 1
 
< 0.1%
0.2415168136 1
 
< 0.1%
0.241546616 11
 
< 0.1%
0.2419116646 1
 
< 0.1%
0.2419128269 18
 
< 0.1%
0.242034927 48
0.1%
ValueCountFrequency (%)
359.9884677 1
< 0.1%
359.9582626 1
< 0.1%
359.8437973 1
< 0.1%
359.7452144 1
< 0.1%
359.6898057 1
< 0.1%
359.6791834 1
< 0.1%
359.6781921 1
< 0.1%
359.6734114 1
< 0.1%
359.6481489 1
< 0.1%
359.6218424 1
< 0.1%

blade_angle
Real number (ℝ)

Distinct17794
Distinct (%)33.9%
Missing72
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean4.8897756
Minimum0
Maximum92.489998
Zeros26317
Zeros (%)50.1%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:25:58.787199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.89733536
95-th percentile44.990002
Maximum92.489998
Range92.489998
Interquartile range (IQR)0.89733536

Descriptive statistics

Standard deviation14.078401
Coefficient of variation (CV)2.8791508
Kurtosis14.199439
Mean4.8897756
Median Absolute Deviation (MAD)0
Skewness3.6235445
Sum256654.54
Variance198.20138
MonotonicityNot monotonic
2023-07-08T17:25:58.881589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26317
50.1%
44.99000168 3008
 
5.7%
0.02449974513 610
 
1.2%
1.49000001 391
 
0.7%
89.98999786 312
 
0.6%
0.0489995709 258
 
0.5%
0.07349946834 161
 
0.3%
0.4900000095 111
 
0.2%
0.0979994285 104
 
0.2%
0.9900000095 82
 
0.2%
Other values (17784) 21134
40.2%
ValueCountFrequency (%)
0 26317
50.1%
0.0001666666552 4
 
< 0.1%
0.0001666666622 27
 
0.1%
0.0001754385918 2
 
< 0.1%
0.0003333333104 3
 
< 0.1%
0.0003333333201 7
 
< 0.1%
0.0003333333244 27
 
0.1%
0.000342105254 2
 
< 0.1%
0.0003508771836 7
 
< 0.1%
0.0003703703605 3
 
< 0.1%
ValueCountFrequency (%)
92.48999786 46
0.1%
92.47216492 1
 
< 0.1%
92.35845431 1
 
< 0.1%
92.20315841 1
 
< 0.1%
92.15666453 5
 
< 0.1%
92.15333303 4
 
< 0.1%
92.14683329 1
 
< 0.1%
92.14333344 9
 
< 0.1%
92.14333344 19
< 0.1%
92.13999939 3
 
< 0.1%

Rear bearing temperature (°C)
Real number (ℝ)

Distinct35931
Distinct (%)68.5%
Missing72
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean65.99655
Minimum13.2275
Maximum76.6525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:25:58.979732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum13.2275
5-th percentile46.386126
Q164.975987
median68.912499
Q370.819999
95-th percentile72.847367
Maximum76.6525
Range63.425
Interquartile range (IQR)5.8440123

Descriptive statistics

Standard deviation8.3977987
Coefficient of variation (CV)0.12724603
Kurtosis6.6433209
Mean65.99655
Median Absolute Deviation (MAD)2.4450005
Skewness-2.42662
Sum3464026.9
Variance70.523023
MonotonicityNot monotonic
2023-07-08T17:25:59.074560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.60000038 14
 
< 0.1%
70.03250008 11
 
< 0.1%
69.55999947 10
 
< 0.1%
69.98750038 10
 
< 0.1%
69.50250015 10
 
< 0.1%
69.97750053 10
 
< 0.1%
70.94749985 9
 
< 0.1%
70.50250015 9
 
< 0.1%
71.60250015 9
 
< 0.1%
70.125 9
 
< 0.1%
Other values (35921) 52387
99.7%
(Missing) 72
 
0.1%
ValueCountFrequency (%)
13.22749996 1
< 0.1%
13.33500004 1
< 0.1%
13.40499973 1
< 0.1%
13.50749969 1
< 0.1%
13.55000019 1
< 0.1%
13.6050005 1
< 0.1%
13.63000011 1
< 0.1%
13.65999985 1
< 0.1%
13.67000008 1
< 0.1%
13.71500015 1
< 0.1%
ValueCountFrequency (%)
76.65250015 1
< 0.1%
76.53000031 1
< 0.1%
76.375 1
< 0.1%
76.225 1
< 0.1%
75.99473813 1
< 0.1%
75.9666659 1
< 0.1%
75.86500015 1
< 0.1%
75.85500031 1
< 0.1%
75.85250015 1
< 0.1%
75.85249977 1
< 0.1%

Rotor speed (RPM)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct51311
Distinct (%)97.8%
Missing72
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean10.862165
Minimum0
Maximum15.321017
Zeros1111
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:25:59.175999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.67804151
Q18.542439
median11.346362
Q314.324177
95-th percentile15.158696
Maximum15.321017
Range15.321017
Interquartile range (IQR)5.781738

Descriptive statistics

Standard deviation3.9502334
Coefficient of variation (CV)0.36366908
Kurtosis1.0546658
Mean10.862165
Median Absolute Deviation (MAD)2.8856476
Skewness-1.1391208
Sum570133.3
Variance15.604344
MonotonicityNot monotonic
2023-07-08T17:25:59.277627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1111
 
2.1%
0.0110000018 10
 
< 0.1%
0.01150000188 6
 
< 0.1%
0.01050000242 3
 
< 0.1%
0.02200000361 3
 
< 0.1%
0.02550000418 3
 
< 0.1%
0.02300000377 3
 
< 0.1%
0.01200000197 3
 
< 0.1%
8.137481689 2
 
< 0.1%
8.142611101 2
 
< 0.1%
Other values (51301) 51342
97.7%
(Missing) 72
 
0.1%
ValueCountFrequency (%)
0 1111
2.1%
0.002955500269 1
 
< 0.1%
0.003813000396 1
 
< 0.1%
0.005106316806 1
 
< 0.1%
0.007560002385 1
 
< 0.1%
0.01050000242 3
 
< 0.1%
0.01050000265 2
 
< 0.1%
0.0110000018 10
 
< 0.1%
0.01125200186 1
 
< 0.1%
0.01150000188 6
 
< 0.1%
ValueCountFrequency (%)
15.32101692 1
< 0.1%
15.30761182 1
< 0.1%
15.30572395 1
< 0.1%
15.30473049 1
< 0.1%
15.30412352 1
< 0.1%
15.30386813 1
< 0.1%
15.30333805 1
< 0.1%
15.30232416 1
< 0.1%
15.29826701 1
< 0.1%
15.28918791 1
< 0.1%

Generator RPM (RPM)
Real number (ℝ)

Distinct52453
Distinct (%)99.9%
Missing72
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1289.3804
Minimum-145.25874
Maximum1818.1705
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size410.8 KiB
2023-07-08T17:25:59.386074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-145.25874
5-th percentile82.156667
Q11015.6784
median1347.1025
Q31698.2031
95-th percentile1797.4821
Maximum1818.1705
Range1963.4293
Interquartile range (IQR)682.52463

Descriptive statistics

Standard deviation467.47911
Coefficient of variation (CV)0.36256104
Kurtosis1.0725356
Mean1289.3804
Median Absolute Deviation (MAD)340.68385
Skewness-1.1457893
Sum67677001
Variance218536.72
MonotonicityNot monotonic
2023-07-08T17:25:59.484315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1723.143888 2
 
< 0.1%
968.2321644 2
 
< 0.1%
968.507019 2
 
< 0.1%
969.1641026 2
 
< 0.1%
968.337759 2
 
< 0.1%
969.356163 2
 
< 0.1%
968.5162354 2
 
< 0.1%
968.6882915 2
 
< 0.1%
968.9338818 2
 
< 0.1%
967.636631 2
 
< 0.1%
Other values (52443) 52468
99.8%
(Missing) 72
 
0.1%
ValueCountFrequency (%)
-145.2587381 1
< 0.1%
-62.29066551 1
< 0.1%
0.9917865358 1
< 0.1%
1.023379591 1
< 0.1%
1.041689957 1
< 0.1%
1.072646328 1
< 0.1%
1.086499862 1
< 0.1%
1.110869776 1
< 0.1%
1.123076677 1
< 0.1%
1.124196371 1
< 0.1%
ValueCountFrequency (%)
1818.170546 1
< 0.1%
1817.766792 1
< 0.1%
1815.020994 1
< 0.1%
1814.897461 1
< 0.1%
1814.094292 1
< 0.1%
1813.883709 1
< 0.1%
1813.078514 1
< 0.1%
1811.976654 1
< 0.1%
1811.869109 1
< 0.1%
1811.751245 1
< 0.1%

Nacelle ambient temperature (°C)
Real number (ℝ)

Distinct32353
Distinct (%)61.6%
Missing72
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean11.432084
Minimum-0.41249999
Maximum36.259999
Zeros0
Zeros (%)0.0%
Negative52
Negative (%)0.1%
Memory size410.8 KiB
2023-07-08T17:25:59.590385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.41249999
5-th percentile3.3000001
Q17.1049999
median10.7075
Q315.36
95-th percentile21.26325
Maximum36.259999
Range36.672499
Interquartile range (IQR)8.255

Descriptive statistics

Standard deviation5.6593388
Coefficient of variation (CV)0.49503999
Kurtosis0.083283499
Mean11.432084
Median Absolute Deviation (MAD)4.0074998
Skewness0.52703577
Sum600047.24
Variance32.028116
MonotonicityNot monotonic
2023-07-08T17:25:59.692674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.5 86
 
0.2%
9 64
 
0.1%
7 58
 
0.1%
8.5 54
 
0.1%
7.099999905 53
 
0.1%
7.699999809 52
 
0.1%
6.400000095 50
 
0.1%
6.800000191 50
 
0.1%
10.80000019 49
 
0.1%
7.800000191 49
 
0.1%
Other values (32343) 51923
98.8%
(Missing) 72
 
0.1%
ValueCountFrequency (%)
-0.412499994 1
 
< 0.1%
-0.400000006 2
< 0.1%
-0.3725000024 1
 
< 0.1%
-0.3300000131 2
< 0.1%
-0.2899999917 1
 
< 0.1%
-0.2100000083 1
 
< 0.1%
-0.200000003 1
 
< 0.1%
-0.1975000054 4
< 0.1%
-0.1925000101 1
 
< 0.1%
-0.1916666627 1
 
< 0.1%
ValueCountFrequency (%)
36.2599987 1
< 0.1%
36.24999886 1
< 0.1%
36.04999924 1
< 0.1%
36.04499931 1
< 0.1%
36.01999969 1
< 0.1%
36.01249981 1
< 0.1%
36.00749989 1
< 0.1%
35.97250042 1
< 0.1%
35.96052632 1
< 0.1%
35.90000134 1
< 0.1%

Front bearing temperature (°C)
Real number (ℝ)

Distinct36237
Distinct (%)69.0%
Missing72
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean66.444896
Minimum14.36
Maximum76.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:25:59.801154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum14.36
5-th percentile45.470875
Q164.0075
median70.55875
Q372.214999
95-th percentile73.845001
Maximum76.75
Range62.39
Interquartile range (IQR)8.2074988

Descriptive statistics

Standard deviation9.2229028
Coefficient of variation (CV)0.13880528
Kurtosis3.9511621
Mean66.444896
Median Absolute Deviation (MAD)2.5312496
Skewness-1.9706649
Sum3487559.7
Variance85.061935
MonotonicityNot monotonic
2023-07-08T17:25:59.895926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71.37750015 11
 
< 0.1%
30.70000076 11
 
< 0.1%
73.75 10
 
< 0.1%
72.375 10
 
< 0.1%
72.55249977 10
 
< 0.1%
70.425 10
 
< 0.1%
73.70249977 10
 
< 0.1%
71.52500038 10
 
< 0.1%
71.34249992 10
 
< 0.1%
70.75 10
 
< 0.1%
Other values (36227) 52386
99.7%
(Missing) 72
 
0.1%
ValueCountFrequency (%)
14.35999966 1
< 0.1%
14.38499928 1
< 0.1%
14.59749985 1
< 0.1%
14.6050005 1
< 0.1%
14.61750031 2
< 0.1%
14.61999989 1
< 0.1%
14.64249992 1
< 0.1%
14.69999981 2
< 0.1%
14.76249981 1
< 0.1%
14.77499962 1
< 0.1%
ValueCountFrequency (%)
76.74999962 1
< 0.1%
76.41250076 1
< 0.1%
76.30499954 1
< 0.1%
76.12000275 1
< 0.1%
76.08611043 1
< 0.1%
76.06750031 1
< 0.1%
76.06749992 1
< 0.1%
76.05750275 1
< 0.1%
76.05000305 1
< 0.1%
76.04999962 1
< 0.1%

Tower Acceleration X (mm/ss)
Real number (ℝ)

Distinct52486
Distinct (%)> 99.9%
Missing71
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean48.313922
Minimum2.9261531
Maximum203.70564
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:26:00.001450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.9261531
5-th percentile4.9506304
Q129.752068
median45.771202
Q363.04923
95-th percentile99.16987
Maximum203.70564
Range200.77948
Interquartile range (IQR)33.297162

Descriptive statistics

Standard deviation27.529654
Coefficient of variation (CV)0.5698079
Kurtosis1.1680875
Mean48.313922
Median Absolute Deviation (MAD)16.587552
Skewness0.8049444
Sum2535949.4
Variance757.88187
MonotonicityNot monotonic
2023-07-08T17:26:00.107571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.44300218 2
 
< 0.1%
52.13734818 2
 
< 0.1%
95.05598364 2
 
< 0.1%
38.23603821 1
 
< 0.1%
20.06645989 1
 
< 0.1%
42.22742648 1
 
< 0.1%
26.10062661 1
 
< 0.1%
44.7460535 1
 
< 0.1%
19.62275081 1
 
< 0.1%
23.94779983 1
 
< 0.1%
Other values (52476) 52476
99.8%
(Missing) 71
 
0.1%
ValueCountFrequency (%)
2.926153075 1
< 0.1%
3.105866456 1
< 0.1%
3.15919385 1
< 0.1%
3.171262705 1
< 0.1%
3.199778843 1
< 0.1%
3.211501519 1
< 0.1%
3.211538941 1
< 0.1%
3.214562161 1
< 0.1%
3.224702352 1
< 0.1%
3.234806532 1
< 0.1%
ValueCountFrequency (%)
203.7056376 1
< 0.1%
199.1851276 1
< 0.1%
197.0186319 1
< 0.1%
195.4129723 1
< 0.1%
192.8822763 1
< 0.1%
190.6154131 1
< 0.1%
189.0354912 1
< 0.1%
188.0130789 1
< 0.1%
187.4152636 1
< 0.1%
183.8962725 1
< 0.1%

Wind speed (m/s)
Real number (ℝ)

Distinct52331
Distinct (%)99.7%
Missing72
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean6.3542588
Minimum0.16530025
Maximum23.731781
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:26:00.217935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.16530025
5-th percentile2.2869999
Q14.4392426
median6.1429734
Q37.9773897
95-th percentile11.074279
Maximum23.731781
Range23.566481
Interquartile range (IQR)3.5381471

Descriptive statistics

Standard deviation2.7596665
Coefficient of variation (CV)0.43430187
Kurtosis0.99588675
Mean6.3542588
Median Absolute Deviation (MAD)1.7697075
Skewness0.6691846
Sum333522.34
Variance7.6157592
MonotonicityNot monotonic
2023-07-08T17:26:00.315656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.94364512 2
 
< 0.1%
8.196148801 2
 
< 0.1%
5.444833541 2
 
< 0.1%
5.734018826 2
 
< 0.1%
5.638867378 2
 
< 0.1%
7.821241999 2
 
< 0.1%
7.299264526 2
 
< 0.1%
6.230064487 2
 
< 0.1%
6.932933521 2
 
< 0.1%
6.007128882 2
 
< 0.1%
Other values (52321) 52468
99.8%
(Missing) 72
 
0.1%
ValueCountFrequency (%)
0.1653002519 1
< 0.1%
0.1960690167 1
< 0.1%
0.2025377585 1
< 0.1%
0.2062502448 1
< 0.1%
0.2197503129 1
< 0.1%
0.2349475175 1
< 0.1%
0.2450813949 1
< 0.1%
0.2594064355 1
< 0.1%
0.2863314956 1
< 0.1%
0.2902876278 1
< 0.1%
ValueCountFrequency (%)
23.7317811 1
< 0.1%
21.99397895 1
< 0.1%
21.53966254 1
< 0.1%
21.00664401 1
< 0.1%
20.8478848 1
< 0.1%
20.5537915 1
< 0.1%
20.38625422 1
< 0.1%
20.29680004 1
< 0.1%
20.25041375 1
< 0.1%
20.23173985 1
< 0.1%

Tower Acceleration y (mm/ss)
Real number (ℝ)

Distinct52476
Distinct (%)> 99.9%
Missing71
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean27.79044
Minimum2.5152512
Maximum283.51459
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:26:00.413989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.5152512
5-th percentile4.9319759
Q116.052702
median24.627358
Q335.645587
95-th percentile59.317918
Maximum283.51459
Range280.99934
Interquartile range (IQR)19.592885

Descriptive statistics

Standard deviation17.70924
Coefficient of variation (CV)0.63724216
Kurtosis6.751346
Mean27.79044
Median Absolute Deviation (MAD)9.5222203
Skewness1.8506205
Sum1458692.4
Variance313.61718
MonotonicityNot monotonic
2023-07-08T17:26:00.507759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.153301239 2
 
< 0.1%
4.574335307 2
 
< 0.1%
4.18824482 2
 
< 0.1%
35.34981704 2
 
< 0.1%
49.38143997 2
 
< 0.1%
27.83149381 2
 
< 0.1%
32.79262924 2
 
< 0.1%
18.74854469 2
 
< 0.1%
13.03123093 2
 
< 0.1%
32.12993622 2
 
< 0.1%
Other values (52466) 52469
99.8%
(Missing) 71
 
0.1%
ValueCountFrequency (%)
2.51525116 1
< 0.1%
2.706247652 1
< 0.1%
2.748020781 1
< 0.1%
2.761294208 1
< 0.1%
3.039628166 1
< 0.1%
3.088215674 1
< 0.1%
3.106091052 1
< 0.1%
3.127113289 1
< 0.1%
3.138592747 1
< 0.1%
3.149679184 1
< 0.1%
ValueCountFrequency (%)
283.5145942 1
< 0.1%
211.4814241 1
< 0.1%
188.9028709 1
< 0.1%
167.2321525 1
< 0.1%
164.7617163 1
< 0.1%
163.3057091 1
< 0.1%
160.3060441 1
< 0.1%
158.544274 1
< 0.1%
157.1001965 1
< 0.1%
156.9820875 1
< 0.1%

Metal particle count counter
Real number (ℝ)

Distinct25
Distinct (%)< 0.1%
Missing71
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean496.505
Minimum482
Maximum506
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:26:00.594424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum482
5-th percentile485
Q1492
median497
Q3503
95-th percentile506
Maximum506
Range24
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.1573175
Coefficient of variation (CV)0.014415399
Kurtosis-1.0874955
Mean496.505
Median Absolute Deviation (MAD)6
Skewness-0.37511173
Sum26061051
Variance51.227194
MonotonicityIncreasing
2023-07-08T17:26:00.679652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
504 6426
12.2%
492 6020
11.5%
493 4810
9.2%
502 3973
 
7.6%
505 3677
 
7.0%
498 3473
 
6.6%
485 3396
 
6.5%
486 3275
 
6.2%
497 3157
 
6.0%
506 3011
 
5.7%
Other values (15) 11271
21.4%
ValueCountFrequency (%)
482 952
 
1.8%
483 1185
 
2.3%
484 94
 
0.2%
485 3396
6.5%
486 3275
6.2%
487 5
 
< 0.1%
488 4
 
< 0.1%
489 799
 
1.5%
490 533
 
1.0%
491 462
 
0.9%
ValueCountFrequency (%)
506 3011
5.7%
505 3677
7.0%
504 6426
12.2%
503 2687
5.1%
502 3973
7.6%
501 1454
 
2.8%
500 54
 
0.1%
499 1166
 
2.2%
498 3473
6.6%
497 3157
6.0%

Interactions

2023-07-08T17:25:55.879343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:41.531269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:42.605472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:43.833051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:44.964759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:46.033870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:47.214603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:48.531001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:49.750833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:50.992289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:52.305873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:53.562853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:54.700747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:55.961378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:41.607885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:42.688068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:43.914182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:45.039665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:46.119266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:47.299959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:48.617360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:49.842911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:51.080970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:52.397766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:53.643133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:54.787207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:56.053116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:41.694485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:42.776607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:44.001811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:45.125271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:46.213717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:47.397903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:48.712753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:49.942264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:51.178228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:52.498672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:53.735561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:54.879998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:56.147880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:41.780540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:42.864453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:44.090946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:45.210008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:46.308851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:47.493338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:48.816665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:50.043844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:51.277689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:52.601414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:53.827506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:54.970995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:56.230187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:41.856058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:42.946622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:44.171576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:45.285333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:46.396463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:47.581128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:48.901877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:50.133048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:51.364237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:52.693743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:53.907363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:55.056905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:56.436558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:41.938624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:43.030504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:44.259594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:45.367039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:46.483288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:47.671043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:48.996093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:50.229610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:51.456830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:52.790279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:53.994566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:55.147042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:56.527125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:42.025765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:43.124942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:44.351850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:45.453588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:46.581337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:47.769800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:49.094462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:50.331834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:51.553638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:52.891245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:54.085850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:55.244670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:56.617751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:42.110012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:43.213950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:44.444291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:45.539698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:46.674167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:47.977040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:49.189469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:50.432654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:51.647211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:52.991488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:54.178278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:55.340974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:56.711214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:42.199341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:43.307023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:44.534262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:45.624716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:46.769597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:48.073774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:49.285035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:50.526926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:51.743041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:53.093469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:54.268451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:55.435775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:56.799266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:42.280100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:43.393180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:44.621286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:45.704014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:46.858285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:48.164719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:49.377603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:50.618971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:51.832762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:53.187957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:54.357164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:55.525792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:56.894096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:42.369804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:43.488325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:44.713718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:45.794113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:46.955316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:48.264375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:49.476610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:50.717741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:52.045847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:53.288453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:54.450437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:55.622448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:56.978154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:42.445505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:43.570740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:44.797485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:45.869242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:47.040875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:48.351205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:49.566264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:50.805003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:52.127060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:53.379321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:54.531431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:55.706988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:57.063726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:42.526417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:43.751968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:44.881118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:45.951782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:47.127778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:48.443090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:49.660001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:50.899117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:52.217560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:53.470495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:54.617785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:25:55.793045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-08T17:26:00.882065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Power (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
Power (kW)1.0000.1700.151-0.1310.6820.9940.994-0.2150.8440.5660.9840.808-0.014
Wind direction (°)0.1701.0000.895-0.0560.1280.1680.168-0.0400.160-0.1010.1810.036-0.082
Nacelle position (°)0.1510.8951.000-0.0320.1050.1480.148-0.0420.139-0.1170.1610.019-0.086
blade_angle-0.131-0.056-0.0321.000-0.480-0.143-0.1450.137-0.3330.061-0.1140.032-0.043
Rear bearing temperature (°C)0.6820.1280.105-0.4801.0000.6810.678-0.0050.8870.3120.6680.4790.056
Rotor speed (RPM)0.9940.1680.148-0.1430.6811.0000.999-0.2100.8450.5610.9780.802-0.011
Generator RPM (RPM)0.9940.1680.148-0.1450.6780.9991.000-0.2200.8440.5600.9780.802-0.013
Nacelle ambient temperature (°C)-0.215-0.040-0.0420.137-0.005-0.210-0.2201.000-0.153-0.077-0.196-0.1330.148
Front bearing temperature (°C)0.8440.1600.139-0.3330.8870.8450.844-0.1531.0000.4230.8280.6440.021
Tower Acceleration X (mm/ss)0.566-0.101-0.1170.0610.3120.5610.560-0.0770.4231.0000.5320.8350.022
Wind speed (m/s)0.9840.1810.161-0.1140.6680.9780.978-0.1960.8280.5321.0000.788-0.009
Tower Acceleration y (mm/ss)0.8080.0360.0190.0320.4790.8020.802-0.1330.6440.8350.7881.000-0.003
Metal particle count counter-0.014-0.082-0.086-0.0430.056-0.011-0.0130.1480.0210.022-0.009-0.0031.000

Missing values

2023-07-08T17:25:57.187130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-08T17:25:57.394016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-08T17:25:57.635206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

# Date and timePower (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
02019-01-01 00:00:00376.141663282.494476292.1936340.00000070.36499810.6158031261.9213878.64500070.86499838.2360385.80052819.262169482.0
12019-01-01 00:10:00348.686066290.551697292.1936340.00000069.22499810.2535631220.0596928.64500069.67749843.6404915.48704518.294140482.0
22019-01-01 00:20:00463.150360288.191071292.1936340.00000071.75499711.2758511338.9632578.66000171.76249748.0233315.76178818.789383482.0
32019-01-01 00:30:00512.391785290.157379292.1936340.00000072.59749611.6387661381.9490978.73000072.71499643.2380376.33774620.295347482.0
42019-01-01 00:40:00384.612122304.097015292.1936340.00000067.75499710.6781091270.1090098.56750068.13749732.9703795.57651615.820447482.0
52019-01-01 00:50:00201.260590312.497528306.1087340.00000066.0750058.9166331060.9575208.60000066.31250043.1941414.62286817.659321482.0
62019-01-01 01:00:00174.496964312.609528313.0473630.07349965.8775028.7462281041.2618418.59500065.65250451.3808254.51290322.204218482.0
72019-01-01 01:10:00203.102905303.017212313.0473630.00000065.9749988.9359931062.2104498.60000065.53749852.9384384.94912221.794411482.0
82019-01-01 01:20:00213.073578298.985535313.0473630.00150066.2150049.0873601081.3837898.53250065.78749841.9173974.96770317.318666482.0
92019-01-01 01:30:00159.261368290.418915311.6426390.07399865.3724988.4930181010.7064218.49250064.96499647.9220704.47503619.388697482.0
# Date and timePower (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
525502019-12-31 22:20:00175.754595105.480339125.3630070.09849865.8299998.7118561036.0739026.850065.37500059.2432814.57033628.186711506.0
525512019-12-31 22:30:00190.74148199.586301125.3630070.00000065.7450008.7875401044.8131036.797565.26250056.9532044.53226428.248822506.0
525522019-12-31 22:40:00134.58708390.608833104.5079430.14749864.6150008.290898986.8079686.725063.93250067.5761694.08958531.775141506.0
525532019-12-31 22:50:00192.86277989.728070101.2167590.02450065.0975018.8248451050.4770986.615064.24499957.8721484.56992728.366423506.0
525542019-12-31 23:00:00172.32552194.381436101.2167590.00000065.1324998.6106531023.1418956.530064.53999980.1622814.18258430.066501506.0
525552019-12-31 23:10:00180.61986192.194368101.2167590.00000065.3149998.6819791033.8922526.267564.68750071.8385144.46109521.168742506.0
525562019-12-31 23:20:00192.81058492.082155101.2167590.00000065.7400018.7888471046.2378296.110065.33750168.8402234.48504121.273681506.0
525572019-12-31 23:30:00259.13995996.061557101.2167590.00000066.3025009.4867801128.0027876.160065.99250064.6405484.73552321.640125506.0
525582019-12-31 23:40:00306.731685103.571461101.2167590.00000067.3850019.9813171186.9095336.257567.35000254.7067465.10358123.263102506.0
525592019-12-31 23:50:00293.376791108.657192101.2167590.00000067.3175009.8291261169.5760486.367567.44000144.6196394.81402419.112631506.0